畳み込みニューラルネットワークを使用した大規模MRIコホートの脳形態の定量化

Tohoku University瀧靖之論

Detecting Outliers in High-Dimensional Neuroimaging Datasets with Robust Covariance Estimators

International audienceMedical imaging datasets often contain deviant observations, the so-called outliers, due to acquisition or preprocessing artifacts or resulting from large intrinsic inter-subject variability. These can undermine the statistical procedures used in group studies as the latter assume that the cohorts are composed of homogeneous samples with anatomical or functional features clustered around a central mode. The effects of outlying subjects can be mitigated by detecting and removing them with explicit statistical control. With the emergence of large medical imaging databases, exhaustive data screening is no longer possible, and automated outlier detection methods are currently gaining interest. The datasets used in medical imaging are often high-dimensional and strongly correlated. The outlier detection procedure should therefore rely on high-dimensional statistical multivariate models. However, state-of-the-art procedures are not well-suited for such high-dimensional settings. In this work, we introduce regularization in the MCD framework and investigate different regularization schemes. We carry out extensive simulations to provide backing for practical choices in absence of ground truth knowledge. We demonstrate on functional neuroimaging datasets that outlier detection can be performed with small sample sizes and improves group studies

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images

[EN] The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.The data collection and labeling of the cerebellum was supported in part by the NIH/NINDS grant R01 NS056307 (PI: J.L. Prince) and NIH/NIMH grants R01 MH078160 & R01 MH085328 (PI: S.H. Mostofsky). PMT is supported in part by the NIH/NIBIB grant U54 EB020403. CERES2 development was supported by grant UPV2016-0099 from the Universitat Politecnica de Valencia (PI: J.V. Manjon); the French National Research Agency through the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project; PI: P. Coupe) and Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57; PI: P. Coupe). Support for the development of LiviaNET was provided by the National Science and Engineering Research Council of Canada (NSERC), discovery grant program, and by the ETS Research Chair on Artificial Intelligence in Medical Imaging. The authors wish to acknowledge the invaluable contributions offered by Dr. George Fein (Dept. of Medicine and Psychology, University of Hawaii) in preparing this manuscript.Carass, A.; Cuzzocreo, JL.; Han, S.; Hernandez-Castillo, CR.; Rasser, PE.; Ganz, M.; Beliveau, V.... (2018). Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images. NeuroImage. 183:150-172. https://doi.org/10.1016/j.neuroimage.2018.08.003S15017218

Oxytocin Receptor Genotype Modulates Ventral Striatal Activity to Social Cues and Response to Stressful Life Events

Background Common variants in the oxytocin receptor gene (OXTR) have been shown to influence social and affective behavior and to moderate the effect of adverse experiences on risk for social-affective problems. However, the intermediate neurobiological mechanisms are not fully understood. Although human functional neuroimaging studies have reported that oxytocin effects on social behavior and emotional states are mediated by amygdala function, animal models indicate that oxytocin receptors in the ventral striatum (VS) modulate sensitivity to social reinforcers. This study aimed to comprehensively investigate OXTR-dependent brain mechanisms associated with social-affective problems. Methods In a sample of 1445 adolescents we tested the effect of 23-tagging single nucleotide polymorphisms across the OXTR region and stressful life events (SLEs) on functional magnetic resonance imaging blood oxygen level-dependent activity in the VS and amygdala to animated angry faces. Single nucleotide polymorphisms for which gene-wide significant effects on brain function were found were then carried forward to examine associations with social-affective problems. Results A gene-wide significant effect of rs237915 showed that adolescents with minor CC-genotype had significantly lower VS activity than CT/TT-carriers. Significant or nominally significant gene × environment effects on emotional problems (in girls) and peer problems (in boys) revealed a strong increase in clinical symptoms as a function of SLEs in CT/TT-carriers but not CC-homozygotes. However, in low-SLE environments, CC-homozygotes had more emotional problems (girls) and peer problems (boys). Moreover, among CC-homozygotes, reduced VS activity was related to more peer problems. Conclusions These findings suggest that a common OXTR-variant affects brain responsiveness to negative social cues and that in "risk- carriers" reduced sensitivity is simultaneously associated with more social-affective problems in "favorable environments" and greater resilience against stressful experiences. © 2014 Society of Biological Psychiatry

Correlations among Brain Gray Matter Volumes, Age, Gender, and Hemisphere in Healthy Individuals

To determine the relationship between age and gray matter structure and how interactions between gender and hemisphere impact this relationship, we examined correlations between global or regional gray matter volume and age, including interactions of gender and hemisphere, using a general linear model with voxel-based and region-of-interest analyses. Brain magnetic resonance images were collected from 1460 healthy individuals aged 20–69 years; the images were linearly normalized and segmented and restored to native space for analysis of global gray matter volume. Linearly normalized images were then non-linearly normalized and smoothed for analysis of regional gray matter volume. Analysis of global gray matter volume revealed a significant negative correlation between gray matter ratio (gray matter volume divided by intracranial volume) and age in both genders, and a significant interaction effect of age × gender on the gray matter ratio. In analyzing regional gray matter volume, the gray matter volume of all regions showed significant main effects of age, and most regions, with the exception of several including the inferior parietal lobule, showed a significant age × gender interaction. Additionally, the inferior temporal gyrus showed a significant age × gender × hemisphere interaction. No regional volumes showed significant age × hemisphere interactions. Our study may contribute to clarifying the mechanism(s) of normal brain aging in each brain region

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions